@article{fdi:010094406,
  title = {{E}stimating channel parameters and discharge at river network scale using hydrological-hydraulic models, {SWOT} and multi-satellite data},
  author = {{L}arnier, {K}. and {G}arambois, {P}. {A}. and {E}mery, {C}. and {P}ujol, {L}eo and {M}onnier, {J}. and {G}al, {L}. and {P}aris, {A}. and {Y}esou, {H}. and {L}edauphin, {T}. and {C}almant, {S}t{\'e}phane},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he unprecedented hydraulic visibility of rivers surfaces deformation with {SWOT} satellite offers tremendous information for improving hydrological-hydraulic models and discharge estimations for rivers worldwide. {H}owever, estimating the uncertain or unknown parameters of hydraulic models, such as inflow discharges, bathymetry, and friction parameters, poses a high-dimensional inverse problem, which is ill-posed if based solely on altimetry observations. {T}o address this, we couple the hydraulic model with a semi-distributed hydrological model, to constrain the ill-posed inverse problem with sufficiently accurate initial estimates of inflows at the network upstreams. {A} robust variational data assimilation of water surface elevation ({WSE}) data into a 1{D} {S}aint-{V}enant river network model, enables the inference of inflow hydrographs, effective bathymetry, and spatially distributed friction at network scale. {T}he method is demonstrated on the large, complex, and poorly gauged {M}aroni basin in {F}rench {G}uiana. {T}he pre-processing chain enables (a) building an effective hydraulic model geometry from drifting {ICES}at-2 {WSE} altimetry and {S}entinel-1 width; (b) filtering noisy {SWOT} {L}evel 2 {WSE} data before assimilation. {A} systematic improvement is achieved in fitting the assimilated {WSE} (85% cost improvement), and in validating discharge at 5 gauges within the network. {F}or assimilation of {SWOT} data alone, 70% of data-model fit is in [-0.25;0.25m] and the discharge {NRMSE} ranges between 0.05 and 0.18 (18%-71% improvement from prior). {T}he high density of {SWOT} {WSE} enables the inferrence of detailed spatial variability in channel bottom elevation and friction, and inflows timeseries. {T}he approach is transferable to other rivers networks worldwide.},
  keywords = {satellite data of {SWOT} ; {ICES}at2 and sentinel 3 altimetry ; sentinel 1 ; images ; saint-venant river network model ; adjoint model ; variational ; data assimilation ; discharge ; bathymetry ; friction ; spatio-temporal ; parameters ; inferrence ; estimation ; calibration ; hydrological-hydraulic ; model ; sequential coupling ; basin ; poorly gauged ; {GUYANE} {FRANCAISE} ; {MARONI} {BASSIN}},
  booktitle = {},
  journal = {{W}ater {R}esources {R}esearch},
  volume = {61},
  numero = {7},
  pages = {e2024{WR}038455 [35 p.]},
  ISSN = {0043-1397},
  year = {2025},
  DOI = {10.1029/2024wr038455},
  URL = {https://www.documentation.ird.fr/hor/fdi:010094406},
}